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Testing for Gene-Environment Interactions Using a Prospective Family Cohort Design: Body Mass Index in Early and Later Adulthood and Risk of Breast Cancer
Author(s) -
Gillian S. Dite,
Robert J. MacInnis,
Adrian Bickerstaffe,
James G. Dowty,
Roger L. Milne,
Antonis C. Antoniou,
Prue C. Weideman,
Carmel Apicella,
Graham G. Giles,
Melissa C. Southey,
Mark A. Jenkins,
KellyAnne Phillips,
Aung Ko Win,
Mary Beth Terry,
John L. Hopper
Publication year - 2016
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kww241
Subject(s) - breast cancer , body mass index , prospective cohort study , medicine , family history , cohort , proportional hazards model , demography , cohort study , cancer , oncology , risk assessment , gerontology , gynecology , computer security , sociology , computer science
The ability to classify people according to their underlying genetic susceptibility to a disease is increasing with new knowledge, better family data, and more sophisticated risk prediction models, allowing for more effective prevention and screening. To do so, however, we need to know whether risk associations are the same for people with different genetic susceptibilities. To illustrate one way to estimate such gene-environment interactions, we used prospective data from 3 Australian family cancer cohort studies, 2 enriched for familial risk of breast cancer. There were 288 incident breast cancers in 9,126 participants from 3,222 families. We used Cox proportional hazards models to investigate whether associations of breast cancer with body mass index (BMI; weight (kg)/height (m)2) at age 18-21 years, BMI at baseline, and change in BMI differed according to genetic risk based on lifetime breast cancer risk from birth, as estimated by BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) software, adjusted for age at baseline data collection. Although no interactions were statistically significant, we have demonstrated the power with which gene-environment interactions can be investigated using a cohort enriched for persons with increased genetic risk and a continuous measure of genetic risk based on family history.

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